In this paper a new algorithm for recognizing 2D objects is introduced. The proposed algorithm is based on searching for first three matched connected lines in both input and model objects, then left and right lines in both input and model objects are marked as matched lines as long as they have the same relations of distance ratio and angle to the last matched and connected lines. The process is repeated until there is no more three matched connected lines. The ratio_test is then performed to detect scattered matched points and lines. The new algorithm is invariant to translations, rotations, reflections and scale changes and has O(m.n) as its computational complexity.
A new approach for form document representation using the maximal grid of its frameset is presented. Using image processing techniques, a scanned form is transformed into a frameset composed of a number of cells. The maximal grid is the grid that encompasses all the horizontal and vertical lines in the form and can be easily generated from the cell coordinates. The number of cells from the original frameset, included in each of the cells created by the maximal grid, is then calculated. Those numbers are added for each row and column generating an array representation for the frameset. A novel algorithm for similarity matching of document framesets based on their maximal grid representations is introduced. The algorithm is robust to image noise and to line breaks, which makes it applicable to poor quality scanned documents. The matching algorithm renders the similarity between two forms as a value between 0 and 1. Thus, it may be used to rank the forms in a database according to their similarity to a query form. Several experiments were performed in order to demonstrate the accuracy and the efficiency of the proposed approach.
In this paper a new algorithm for recognizing handwritten Hindi digits is proposed. The proposed algorithm is based on using the topological characteristics combined with statistical properties of the given digits in order to extract a set of features that can be used in the process of digit classification. 10,000 handwritten digits are used in the experimental results. 1100 digits are used for training and another 5500 unseen digits are used for testing. The recognition rate has reached 97.56%, a substitution rate of 1.822%, and a rejection rate of 0.618%.